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  1. This survey discusses a posteriori error estimation for model order reduction of parametric systems, including linear and nonlinear, time-dependent and steady systems. We focus on introducing the error estimat...

    Authors: Lihong Feng, Sridhar Chellappa and Peter Benner
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2024 11:5
  2. Materials with sufficient strength and stiffness can transfer nonlinear design loads without damage. The present study compares crack propagation speed and shape in rock-like material and sandstone when subjec...

    Authors: Omer Mughieda, Lijie Guo, Yunchao Tang, Nader M. Okasha, Sayed Javid Azimi, Abdoullah Namdar and Falak Azhar
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2024 11:4
  3. The simulation of magnetic bearings involves highly non-linear physics, with high dependency on the input variation. Moreover, such a simulation is time consuming and can’t run, within realistic computation ti...

    Authors: Chady Ghnatios, Sebastian Rodriguez, Jerome Tomezyk, Yves Dupuis, Joel Mouterde, Joaquim Da Silva and Francisco Chinesta
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2024 11:3
  4. We present a fast high-order scheme for the numerical solution of a volume-surface integro-differential equation. Such equations arise in problems of scattering of time-harmonic acoustic and electromagnetic wa...

    Authors: Jagabandhu Paul, Ambuj Pandey, B. V. Rathish Kumar and Akash Anand
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2024 11:2
  5. Physical systems whose dynamics are governed by partial differential equations (PDEs) find numerous applications in science and engineering. The process of obtaining the solution from such PDEs may be computat...

    Authors: Pratyush Bhatt, Yash Kumar and Azzeddine Soulaïmani
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:17
  6. Computational process modelling of metal additive manufacturing has gained significant research attention in recent past. The cornerstone of many process models is the transient thermal response during the AM ...

    Authors: Rajit Ranjan, Matthijs Langelaar, Fred Van Keulen and Can Ayas
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:15
  7. Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome ...

    Authors: Ziyi Yin, Rafael Orozco, Mathias Louboutin and Felix J. Herrmann
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:14
  8. The multiscale evaluation method is applied to assess the influence of detailed geometric modeling of trees on their macroscopic attenuation effect against tsunami-like flow. Specifically, we conduct a series ...

    Authors: Reika Nomura, Shinsuke Takase, Shuji Moriguchi and Kenjiro Terada
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:13
  9. In the present work, we introduce a novel approach to enhance the precision of reduced order models by exploiting a multi-fidelity perspective and DeepONets. Reduced models provide a real-time numerical approx...

    Authors: Nicola Demo, Marco Tezzele and Gianluigi Rozza
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:12
  10. This study presents a novel wave–structure interaction model, which is a compatible interface wave–structure interaction model that is based on mesh-free particle methods for free-surface flow analysis; the FE...

    Authors: Naoto Mitsume
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:11
  11. We are interested in the modelling of saturated thermo-hydro-mechanical (THM) problems that describe the behaviour of a soil in which a weakly compressible fluid evolves. It is used for the evaluation of the T...

    Authors: Ana C. Ordonez, Nicolas Tardieu, Carola Kruse, Daniel Ruiz and Sylvie Granet
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:10
  12. Calibration of complex system models with a large number of parameters using standard optimization methods is often extremely time-consuming and not fully automated due to the reliance on all-inclusive expert ...

    Authors: Yi Zhang and Lars Mikelsons
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:9
  13. Lightweight construction in modern car design leads to an increased usage of various aluminium semi-finished products. Besides sheet material, aluminium extrusion profiles are frequently used due to their high...

    Authors: Hannes Fröck, Matthias Graser, Michael Reich, Michael Lechner, Marion Merklein and Olaf Kessler
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:8
  14. A non-intrusive reduced-order model based on convolutional autoencoders is proposed as a data-driven tool to build an efficient nonlinear reduced-order model for stochastic spatiotemporal large-scale flow prob...

    Authors: Azzedine Abdedou and Azzeddine Soulaimani
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:7
  15. We introduce a novel hybrid methodology that combines classical finite element methods (FEM) with neural networks to create a well-performing and generalizable surrogate model for forward and inverse problems....

    Authors: Rishith E. Meethal, Anoop Kodakkal, Mohamed Khalil, Aditya Ghantasala, Birgit Obst, Kai-Uwe Bletzinger and Roland Wüchner
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:6
  16. We present a Reduced Order Model (ROM) which exploits recent developments in Physics Informed Neural Networks (PINNs) for solving inverse problems for the Navier–Stokes equations (NSE). In the proposed approac...

    Authors: Saddam Hijazi, Melina Freitag and Niels Landwehr
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:5
  17. Regressions created from experimental or simulated data enable the construction of metamodels, widely used in a variety of engineering applications. Many engineering problems involve multi-parametric physics w...

    Authors: Abel Sancarlos, Victor Champaney, Elias Cueto and Francisco Chinesta
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:4
  18. Most of the recently developed methods for predicting instabilities of frictional systems couple stochastic algorithms with the finite element method (FEM). They use random variables to model the uncertainty o...

    Authors: Farouk Maaboudallah and Noureddine Atalla
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:3
  19. We propose a new damage model for simulating the cohesive fracture behavior of multi-phase composite materials such as concrete. The proposed model can evaluate the damage of the matrix-phase in composite mate...

    Authors: Mao Kurumatani, Takumi Kato and Hiromu Sasaki
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:2
  20. This study develops a new numerical simulation model for rubble mound failure prediction caused by piping destruction under seepage flows. The piping has been pointed out as a significant cause of breakwater f...

    Authors: Kumpei Tsuji, Mitsuteru Asai and Kiyonobu Kasama
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:1
  21. Calibration or parameter identification is used with computational mechanics models related to observed data of the modeled process to find model parameters such that good similarity between model prediction a...

    Authors: Harald Willmann, Jonas Nitzler, Sebastian Brandstäter and Wolfgang A. Wall
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:24
  22. We propose an adaptive moment-matching framework for model order reduction of quadratic-bilinear systems. In this framework, an important issue is the selection of those shift frequencies where moment-matching...

    Authors: Muhammad Altaf Khattak, Mian Ilyas Ahmad, Lihong Feng and Peter Benner
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:23
  23. The multiscale method called Pseudo-Direct Numerical Simulation (P-DNS) is presented as a Reduced Order Model (ROM) aiming to solve problems obtaining similar accuracy to a solution with many degrees of freedo...

    Authors: Sergio R. Idelsohn, Juan M. Gimenez and Norberto M. Nigro
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:22
  24. Simulation-based engineering has been a major protagonist of the technology of the last century. However, models based on well established physics fail sometimes to describe the observed reality. They often ex...

    Authors: Francisco Chinesta and Elias Cueto
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:21
  25. The numerical modelling of natural disasters such as landslides presents several challenges for conventional mesh-based methods such as the finite element method (FEM) due to the presence of numerically challe...

    Authors: Jonghyuk Baek, Ryan T. Schlinkman, Frank N. Beckwith and Jiun-Shyan Chen
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:20
  26. In this work a multi-point constraint unfitted finite element method for the solution of the Poisson equation is presented. Key features of the approach are the strong enforcement of essential boundary, and in...

    Authors: Brubeck Lee Freeman
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:19
  27. In this contribution, the accuracy and efficiency of various modeling assumptions and numerical settings in thermo-mechanical simulations of powder bed fusion (PBF) processes are analyzed. Thermo-mechanical si...

    Authors: Christian Burkhardt, Paul Steinmann and Julia Mergheim
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:18
  28. Most of the methods used today for handling local stress constraints in topology optimization, fail to directly address the non-self-adjointness of the stress-constrained topology optimization problem. This in...

    Authors: Manyu Xiao, Jun Ma, Dongcheng Lu, Balaji Raghavan and Weihong Zhang
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:17
  29. Mass-movement hazards involving fast and large soil deformation often include huge rocks or other significant obstacles increasing tremendously the risks for humans and infrastructures. Therefore, numerical in...

    Authors: Veronika Singer, Klaus B. Sautter, Antonia Larese, Roland Wüchner and Kai-Uwe Bletzinger
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:16
  30. This work presents a partitioned method for landslide-generated wave events. The proposed strategy combines a Lagrangian Navier Stokes multi-fluid solver with an Eulerian method based on the Boussinesq shallow...

    Authors: Miguel Masó, Alessandro Franci, Ignasi de-Pouplana, Alejandro Cornejo and Eugenio Oñate
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:15
  31. The landslide surge is a common secondary disaster of reservoir bank landslides, which can cause more serious damage than the landslide itself in many cases. With the development of large-scale scientific and ...

    Authors: Yinghan Wu, Kaixuan Shao, Francesco Piccialli and Gang Mei
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:14
  32. The authors have developed a novel physics-based nonlinear autoregressive exogeneous neural network model architecture for flight modelling across the entire flight envelope, called FlyNet. When using traditional...

    Authors: Terrin Stachiw, Alexander Crain and Joseph Ricciardi
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:13
  33. The embedded finite element technique provides a unique approach for modeling of fiber-reinforced composites. Meshing fibers as distinct bundles represented by truss elements embedded in a matrix material mesh...

    Authors: Valerie A. Martin, Reuben H. Kraft, Thomas H. Hannah and Stephen Ellis
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:12
  34. In recent times, artificial neural networks (ANNs) have become the popular choice of model for researchers while performing regression analysis between inputs and output. However; in scientific and engineering...

    Authors: E. Rajasekhar Nicodemus
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:11
  35. The behavior of many physical systems is described by means of differential equations. These equations are usually derived from balance principles and certain modelling assumptions. For realistic situations, t...

    Authors: Sebastián Cedillo, Ana-Gabriela Núñez, Esteban Sánchez-Cordero, Luis Timbe, Esteban Samaniego and Andrés Alvarado
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:10
  36. This work addresses research questions arising from the application of geometrically exact beam theory in the context of fluid-structure interaction (FSI). Geometrically exact beam theory has proven to be a co...

    Authors: Nora Hagmeyer, Matthias Mayr, Ivo Steinbrecher and Alexander Popp
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:9
  37. Many real world problems involve fluid flow phenomena, typically be described by the Navier–Stokes equations. The Navier–Stokes equations are partial differential equations (PDEs) with highly nonlinear propert...

    Authors: Jan Oldenburg, Finja Borowski, Alper Öner, Klaus-Peter Schmitz and Michael Stiehm
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:8
  38. Most real optimization problems are defined over a mixed search space where the variables are both discrete and continuous. In engineering applications, the objective function is typically calculated with a nu...

    Authors: Jhouben Cuesta Ramirez, Rodolphe Le Riche, Olivier Roustant, Guillaume Perrin, Cédric Durantin and Alain Glière
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:6
  39. Nowadays, in the Scientific Machine Learning (SML) research field, the traditional machine learning (ML) tools and scientific computing approaches are fruitfully intersected for solving problems modelled by Pa...

    Authors: Fabio Giampaolo, Mariapia De Rosa, Pian Qi, Stefano Izzo and Salvatore Cuomo
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:5
  40. Identification from field measurements allows several parameters to be identified from a single test, provided that the measurements are sensitive enough to the parameters to be identified. To do this, authors...

    Authors: Morgane Chapelier, Robin Bouclier and Jean-Charles Passieux
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:4
  41. In this work, we propose a new method to fill the gap within an incomplete turbulent and incompressible data field in such a way to satisfy the topological and intensity changes of the fluid flow after a non-p...

    Authors: Nissrine Akkari, Fabien Casenave, David Ryckelynck and Christian Rey
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:3
  42. A domain decomposition technique combined with an enhanced geometry mapping based on the use of NURBS is considered for solving parametrized models in complex geometries (non simply connected) within the so-ca...

    Authors: Mohammad Javad Kazemzadeh-Parsi, Amine Ammar and Francisco Chinesta
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:2
  43. In design optimization of complex systems, the surrogate model approach relying on progressively enriched Design of Experiments (DOE) avoids efficiency problems encountered when embedding simulation codes with...

    Authors: Hanane Khatouri, Tariq Benamara, Piotr Breitkopf and Jean Demange
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:1
  44. Although projection-based reduced-order models (ROMs) for parameterized nonlinear dynamical systems have demonstrated exciting results across a range of applications, their broad adoption has been limited by t...

    Authors: Zhe Bai and Liqian Peng
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2021 8:28
  45. In this study, we applied an advanced barycentric Lagrange interpolation formula to find the interpolate solutions of weakly singular Fredholm integral equations of the second kind. The kernel is interpolated ...

    Authors: E. S. Shoukralla, Nermin Saber and Ahmed Y. Sayed
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2021 8:27
  46. Solutions of partial differential equations can exhibit multiple time scales. Standard discretization techniques are constrained to capture the finest scale to accurately predict the response of the system. In...

    Authors: Angelo Pasquale, Amine Ammar, Antonio Falcó, Simona Perotto, Elías Cueto, Jean-Louis Duval and Francisco Chinesta
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2021 8:26
  47. Surrogate modelling is a powerful tool to replace computationally expensive nonlinear numerical simulations, with fast representations thereof, for inverse analysis, model-based control or optimization. For so...

    Authors: Boukje M. de Gooijer, Jos Havinga, Hubert J. M. Geijselaers and Anton H. van den Boogaard
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2021 8:25